In-class_Ex03

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pacman::p_load(sf, spNetwork, tmap, tidyverse)

Data Import and Preparation

network <- st_read(dsn="data/geospatial", 
                   layer="Punggol_St")

Reading layer `Punggol_St' from data source 
  `/Users/lucasluo/Desktop/SMU/Courses/Term3 Aug-Dec/ISSS626-Geospatial Analytics and Applications/lucasluo6/ISSS626/In-class-Ex/In-class_Ex03/data/geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 2642 features and 2 fields
Geometry type: LINESTRING
Dimension:     XY
Bounding box:  xmin: 34038.56 ymin: 40941.11 xmax: 38882.85 ymax: 44801.27
Projected CRS: SVY21 / Singapore TM

childcare <- st_read(dsn="data/geospatial",
                     layer="Punggol_CC")

Reading layer `Punggol_CC' from data source 
  `/Users/lucasluo/Desktop/SMU/Courses/Term3 Aug-Dec/ISSS626-Geospatial Analytics and Applications/lucasluo6/ISSS626/In-class-Ex/In-class_Ex03/data/geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 61 features and 1 field
Geometry type: POINT
Dimension:     XYZ
Bounding box:  xmin: 34423.98 ymin: 41503.6 xmax: 37619.47 ymax: 44685.77
z_range:       zmin: 0 zmax: 0
Projected CRS: SVY21 / Singapore TM

childcare_01 <-st_zm(childcare,drop = TRUE,
        what = "ZM")

Visualising the Geospatial Data

plot(st_geometry(network))
plot(childcare,add=T,col='red',pch = 19)

tmap_mode("view")

tmap mode set to interactive viewing

tm_shape(childcare)+
  tm_dots(col ="red")+
tm_shape(network)+
  tm_lines()

tmap_mode('plot')

tmap mode set to plotting

##Preparing the lixels objects

lixels <- lixelize_lines(network, 
                         700, 
                         mindist = 350)
samples <- lines_center(lixels) 

tmap_mode('view')

tmap mode set to interactive viewing

tm_shape(lixels) +
  tm_lines(col = 'blue') +
  tm_shape(samples) +
  tm_dots() 

tmap_mode('plot')

tmap mode set to plotting

# Performing NKDE
densities <- nkde(network, 
                  events = childcare,  # Ensure childcare is an 'sf' object
                  w = rep(1, nrow(childcare)),  # Weights corresponding to each point in childcare
                  samples = samples,  # Centers of lixels as samples
                  kernel_name = "quartic",
                  bw = 350, 
                  div = "bw",
                  method = "simple", )

visualising NKDE

samples$density <- densities
lixels$density <- densities

tmap_mode('view')
tm_shape(lixels)+
  tm_lines(col="density")+
tm_shape(childcare)+
  tm_dots()
tmap_mode('plot')

kfun_childcare <- kfunctions(network, 
                             childcare,
                             start = 0, 
                             end = 1000, 
                             step = 50, 
                             width = 50, 
                             nsim = 50, 
                             resolution = 50,
                             verbose = FALSE, 
                             conf_int = 0.05)
kfun_childcare$plotk